Adaptive methods such as adaptive filters are employed in echo-cancellers because adaptive methods can adjust their algorithms according to fluctuations in incoming speech signals. However, adaptive methods employed in echo-cancellers (ECs) may have problems removing echoes of speech signals completely due to the fundamental limitations related with convergence speed and steady-state performance. This is often true for normalized least mean square (NLMS) based methods that are used in the industry. To compensate for these problems, non-linear processors (NLPs) may also be included in an adaptive method to further process a residual echo signal.
NLPs use a non-linearity such as a center-clipper shown in FIG. 1 to remove a residual echo signal. However, in removing a residual echo signal, too much energy may be suppressed. For example in FIG. 1, a center-clipper may remove the input signal that falls into hatched region 101. To replace some of the suppressed energy, different approaches may be taken. One approach is to use a noise injection scheme to replace the suppressed energy. This may involve simple injection of spectrally shaped or spectrally matched noise.
While schemes based on the above ideas may be implemented in practice and are improvements of prior approaches, their performances leave room for improvement with the increased demand for voice quality. FIG. 2 illustrates one of these schemes. FIG. 2 shows an echo canceller (EC) that includes an adaptive filter 201 and a non-linear processor (NLP) 202. Consider the EC operating in a condition where the send-in (Sin) port at line 203 of the EC has a low-level coherent background signal such as music, people chatting, etc. The portions of the signal that have this background signal typically pass through the NLP practically unaltered. However, when there is echo and background signals simultaneously present in the Sin port, the NLP while removing the residual echo may also remove the background signal. Because the noise injection scheme in the NLP may not completely restore the integrity of the background signal to an acceptable level, a non-consistent background may be produced.
Because many linear processing methods do not completely separate the residual echo from the background signal, the objective of further removing the residual echo while preserving the integrity of the background signal creates opportunities to improve these methods. Thus a method and apparatus for more effectively managing speech signals that may include both a residual echo and a background signal is needed.